In general, an image of a scene is a two-dimensional brightness map, each point of which represents a brightness value that is a product of illumination intensity and surface reflectance associated with a portion of the scene imaged to that point. Decomposing this product into illumination and surface reflectance components, or in other words, differentiating variations in illumination from those in surface reflectance or surface color, can be useful in a variety of applications. If the effects of non-uniform illumination can be removed from an image, the changes of surface reflection can be attributed to true material changes.
For example, Edwin Land demonstrated that human color perception is predicated upon estimates of reflectance properties of a viewed surface in three wavelength bands, rather than upon proportions of red, green and blue wavelengths emanating from that surface. More specifically, Land proposed that the eye performs a spatial computation to generate three surface reflectance estimates, each corresponding to one of its three color sensitivity systems, and that these three estimates, which he called lightness designators, define human color perception. Because illumination of a scene may not be uniform, determining these relative reflectance estimates generally requires removing the effects of variations in illumination.
A number of techniques are known in the art for identifying variations in illumination (recognizing shadows) in an image of a scene. Some of these techniques are based on the assumption that shadow boundaries are characterized by gradients. For example, a number of algorithms, such as the Land-McCann algorithm, utilize a spatial thresholding approach in which gradual brightness transitions, i.e., brightness gradients below a defined threshold, are assumed to be due to variations in illumination, while those exhibiting gradients above the threshold are identified as material boundaries.
Such conventional algorithms, however, suffer from a number of shortcomings. First, not all illumination boundaries are gradients. For example, an image of a white cube illuminated with a single spotlight will have boundaries along the edges of the cube that are characterized by stepwise (sharp) variations in illumination. Spatial thresholding techniques would incorrectly classify such illumination boundaries as material boundaries. Defining a threshold value presents another problem. For example, a boundary imaged at a great distance may appear sharp, but may exhibit a gradual gradient when imaged at a closer distance. Further, some material boundaries may in fact exhibit gradual gradients, for example, a boundary characterized by one color blending into another.
Hence, there is a need for enhanced methods for distinguishing illumination boundaries from material (reflectance) boundaries in an image. There is also a need for such methods that would correctly differentiate material and illumination boundaries even when sharp illumination boundaries are present in the image.